Modern recommender systems are usually trained using implicit feedback data. The training of implicit recommenders usually needs to sample negative examples from missing interactions and then feed the interacted items and sampled negative instances for pair-wise ranking [1]} or BCE loss function. Besides, there are also attempts to investigate non-sampling approaches [2]}, [3]}, [4]} for implicit feedback.
Regarding the recommendation models, MF [5]} is one of the most notable and effective models, which projects users and items to embedding vectors and then calculate the inner product between them as the prediction score. Recently, plenty of work has proposed deep learning-based recommendation models, such as GMF, NeuMF[6]}, CDAE[7]} and Wide&Deep [8]}. The key idea is to use deep learning to increase model expressiveness to capture more complex signals. Besides, graph neural networks also demonstrated their capability in recommendation.
Plenty of models have emerged, such as HOP-Rec [9]}, KGAT [10]}, NGCF [11]} and LightGCN [12]}. This work aims at developing denoising learning methods which can be used to train various kinds of models, other than a specific recommendation model.
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